Does mitigating ML's impact disparity require treatment disparity?
Authors: Zachary Lipton, Julian McAuley, Alexandra Chouldechova
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several real-world datasets highlight the practical consequences of applying DLPs. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2University of California, San Diego |
| Pseudocode | No | The paper describes procedures in narrative form (e.g., 'Our thresholding rule for maximizing accuracy subject to a p-% rule works as follows...'), but does not include structured pseudocode blocks or algorithms. |
| Open Source Code | No | The paper states 'code and data will be released at publication time', which is a promise for future release, not concrete access. It also mentions using a third-party code: 'We apply the DLP proposed by Zafar et al. [5], using code available from the authors.2 https://github.com/mbilalzafar/fair-classification/' |
| Open Datasets | Yes | To construct the data, we sample nall = 2000 total observations from the data-generating process described below. 70% of the observations are used for training, and the remaining 30% are reserved for model testing. ... We consider a sample of 9,000 students considered for admission ... Half of the examples are withheld for testing. ... Statistics of public datasets. Income UCI [22] ... Marketing UCI [23] ... Credit UCI [24] ... Employee Attr. IBM [25] ... Customer Attr. IBM [25] |
| Dataset Splits | No | The paper specifies training and testing splits (e.g., '70% of the observations are used for training, and the remaining 30% are reserved for model testing' and 'Half of the examples are withheld for testing'), but does not explicitly mention a separate validation split or strategy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, cloud instance types) used for running experiments. |
| Software Dependencies | No | The paper mentions applying a DLP from Zafar et al. [5] and logistic regressors, but does not provide specific software names with version numbers for reproducibility (e.g., Python, PyTorch, scikit-learn versions). |
| Experiment Setup | No | The paper describes the data-generating process and mentions training logistic regressors, but does not specify concrete hyperparameters (e.g., learning rate, batch size, number of epochs, optimizer settings) or other system-level training configurations for these models. |